@InProceedings{Cao:2016:EuroGP,
author = "Van Loi Cao and Miguel Nicolau and James McDermott",
title = "One-class Classification for Anomaly Detection with
Kernel Density Estimation and Genetic Programming",
booktitle = "EuroGP 2016: Proceedings of the 19th European
Conference on Genetic Programming",
year = "2016",
month = "30 " # mar # "--1 " # apr,
editor = "Malcolm I. Heywood and James McDermott and
Mauro Castelli and Ernesto Costa and Kevin Sim",
series = "LNCS",
volume = "9594",
publisher = "Springer Verlag",
address = "Porto, Portugal",
pages = "3--18",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, Anomaly
detection, One-class classification, Kernel Density
Estimation",
isbn13 = "978-3-319-30668-1",
DOI = "doi:10.1007/978-3-319-30668-1_1",
abstract = "A novel approach is proposed for fast anomaly
detection by one-class classification. Standard kernel
density estimation is first used to obtain an estimate
of the input probability density function, based on the
one-class input data. This can be used for anomaly
detection: query points are classed as anomalies if
their density is below some threshold. The disadvantage
is that kernel density estimation is lazy, that is the
bulk of the computation is performed at query time. For
large datasets it can be slow. Therefore it is proposed
to approximate the density function using genetic
programming symbolic regression, before imposing the
threshold. The runtime of the resulting genetic
programming trees does not depend on the size of the
training data. The method is tested on datasets
including in the domain of network security. Results
show that the genetic programming approximation is
generally very good, and hence classification accuracy
approaches or equals that when using kernel density
estimation to carry out one-class classification
directly. Results are also generally superior to
another standard approach, one-class support vector
machines.",
notes = "Part of \cite{Heywood:2016:GP}EuroGP'2016 held in
conjunction with EvoCOP2016, EvoMusArt2016 and
EvoApplications2016",
}